Recently, research for recognizing human activity and facial expression has advanced thanks to proactive computing. Proactive computing focuses on the health related needs of people by studying solutions in advance, and intends to take necessary actions on their behalf.
Facial expression and human activity recognition is regarded as a fundamental technique in Human Computer Interaction (HCI). HCI enables the computer to interrelate with humans such as human to human interaction.
Accordingly, facial expression and human activity recognition can contribute to the design of a HCI system which responds to the expressive states of human and human behaviors.
Typically, in the general shape-based human activity recognition technology, binary shapes are commonly used to represent different human activities.
In the binary representation, some body components (e.g., arms) are commonly hidden in the binary shapes of different activities (e.g., clapping). This causes ambiguities when the same binary shape is assigned to different activities. Hence, efficient representation of the human body in the different activities is quite important.
Mostly, Principal Component Analysis (PCA), which is a second-order statistical approach, is used to decrease the dimension of the database including human activity images. Basically, PCA basis images are orthogonal to each other and represent global features focusing on the body components which are frequently used in the activity.
In general approaches, the PCA features are typically combined with Hidden Markov Model (HMM) to recognize different human activities. However, the PCA representation of the binary shape of the human activity exhibits a low recognition rate, which is proven to be inefficient.
In general Facial Expression Recognition (FER), there are several methods for recognize facial expressions. One of the methods identifies the muscle movement in the face according to changes in Facial Action Units (FAUs).
Another method separates the holistic facial expressions corresponding to the global expressions such as joy, anger, disgust, fear, and sadness, using the PCA.
Lately, to distinguish facial expressions on the type basis, Independent Component Analysis (ICA) method with its function for extracting local features is excessively used in the FER tasks.
The ICA method reduces the statistical dependency of a set of input random variables to generate the statistically independent basis and coefficients and is popularly used to recognize the facial expressions.
However, it turns out that many techniques attempted have revealed their limitations and difficulties. The FER based on the FAU using the general ICA has a huge potential for the expressions. The expression varies based on the diverse combination of the FAUs, which causes a very complicated FER problem.
Moreover, because temporal variations in the face are assembled into a specific expression, the generic ICA employed for the holistic facial representation has been applied only to the static images of apex expression for extracting inadequate spatial information.
However, the temporal information needs to be deliberated because of the FER problem.